AutoAnalyze in Systems Biology

Saad, Christian; Bauer, Bernhard; Mansmann, Ulrich; Li, Jian

doi:10.1177/1177932218818458

Cited by 2 publications

(2 citation statements)

References 17 publications

(18 reference statements)

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“…The Definition of IM.Index The IM.Index was defined as a score index to summarize the flux from multiple immune system related pathways and its application was to predict the resistance of anti-PD-1 therapy. A network-oriented data-flow analysis, AutoAnalysis 39 , was applied to conduct the calculation of IM.Index. The formula of an IM-Index of a given patient with gene expression profile (GEP) is defined following (Supplement Information):…”

Section: Genome-scale Pathway Flux Analysis (Gpfa)mentioning

confidence: 99%

Genome-scale Pathway Flux Analysis Predicts Efficacy of anti-PD-1 Therapy in Melanoma

Mei¹,

Du²,

Halfter³

et al. 2020

Preprint

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Background: Currently, predicting treatment efficacy to immunotherapy has been under extensive investigation. However, a putative biomarker for immunotherapy in melanoma remains to be found.Methods: Utilizing genetic data from two independent melanoma patient cohorts treated with anti-PD-1 therapy, the study described herein conducted a genome-scale pathway flux analysis (GPFA) and related statistical methods to determine whether specific pathways could be identified that are relevant to immunotherapy efficacy.Results: The analysis results highlighted three mechanisms responsible for the efficacy of immunotherapy in melanoma including 1) proper cellular functions in immune cells; 2) angiogenesis for the development and differentiation of immune cells; 3) energy metabolic remodeling to meet the activation of immune cells. Based on these discoveries, a pathway flux-based biomarker (IM.Index) was developed and assessed to validate its predictive ability with odds ratio (OR) of 3.14 (95%CI: 1.16-8.45; p=3.10E-3), sensitivity 76% and specificity 89%. The IM.Index achieved an objective response rate (ORR) of 70%. Comparison to other four putative biomarkers (TMB, NAL, neo-peptide load and cytolytic score) showed a comparative outcome with an hazard ratio (HR) of 1.83 (95%CI: 1.26-2.67; p=1.62E-3) and area under curve (AUC) of 0.82.Conclusion: These results indicate a translational potential of IM.Index, as a biomarker, for anti-PD-1 therapy in melanoma and the GPFA might pave a new path for biomarker discovery in immunotherapy.

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Section: Genome-scale Pathway Flux Analysis (Gpfa)mentioning

confidence: 99%

Genome-scale Pathway Flux Analysis Predicts Efficacy of anti-PD-1 Therapy in Melanoma

Mei¹,

Du²,

Halfter³

et al. 2020

Preprint

Self Cite

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“…Other studies tried to identify molecular signatures at the DNA, RNA and epigenetic levels by comparing tumor and normal tissues. However, the resultant molecular signatures varied significantly due to the heterogeneity of the tumor tissue and the applied statistical approaches 34 – 36 .…”

Section: Introductionmentioning

confidence: 99%

Differential early diagnosis of benign versus malignant lung cancer using systematic pathway flux analysis of peripheral blood leukocytes

Li¹,

et al. 2022

Sci Rep

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Early diagnosis of lung cancer is critically important to reduce disease severity and improve overall survival. Newer, minimally invasive biopsy procedures often fail to provide adequate specimens for accurate tumor subtyping or staging which is necessary to inform appropriate use of molecular targeted therapies and immune checkpoint inhibitors. Thus newer approaches to diagnosis and staging in early lung cancer are needed. This exploratory pilot study obtained peripheral blood samples from 139 individuals with clinically evident pulmonary nodules (benign and malignant), as well as ten healthy persons. They were divided into three cohorts: original cohort (n = 99), control cohort (n = 10), and validation cohort (n = 40). Average RNAseq sequencing of leukocytes in these samples were conducted. Subsequently, data was integrated into artificial intelligence (AI)-based computational approach with system-wide gene expression technology to develop a rapid, effective, non-invasive immune index for early diagnosis of lung cancer. An immune-related index system, IM-Index, was defined and validated for the diagnostic application. IM-Index was applied to assess the malignancies of pulmonary nodules of 109 participants (original + control cohorts) with high accuracy (AUC: 0.822 [95% CI: 0.75–0.91, p < 0.001]), and to differentiate between phases of cancer immunoediting concept (odds ratio: 1.17 [95% CI: 1.1–1.25, p < 0.001]). The predictive ability of IM-Index was validated in a validation cohort with a AUC: 0.883 (95% CI: 0.73–1.00, p < 0.001). The difference between molecular mechanisms of adenocarcinoma and squamous carcinoma histology was also determined via the IM-Index (OR: 1.2 [95% CI 1.14–1.35, p = 0.019]). In addition, a structural metabolic behavior pattern and signaling property in host immunity were found (bonferroni correction, p = 1.32e − 16). Taken together our findings indicate that this AI-based approach may be used for “Super Early” cancer diagnosis and amend the current immunotherpay for lung cancer.

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AutoAnalyze in Systems Biology

Cited by 2 publications

References 17 publications

Genome-scale Pathway Flux Analysis Predicts Efficacy of anti-PD-1 Therapy in Melanoma

Genome-scale Pathway Flux Analysis Predicts Efficacy of anti-PD-1 Therapy in Melanoma

Differential early diagnosis of benign versus malignant lung cancer using systematic pathway flux analysis of peripheral blood leukocytes

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